code: def fit(
      self,
      base_models_predictions: List[np.ndarray],
      true_targets: np.ndarray,
      model_identifiers: List[Tuple[int, int, float]],
      runs: Sequence[Run],
      X_data: SUPPORTED_FEAT_TYPES | None = None,
  ) -> EnsembleSelection:
      self.ensemble_size = int(self.ensemble_size)
      if self.ensemble_size < 1:
          raise ValueError("Ensemble size cannot be less than one!")
      if self.task_type not in TASK_TYPES:
          raise ValueError("Unknown task type %s." % self.task_type)
      if not isinstance(self.metric, Scorer):
          raise ValueError(
              "The provided metric must be an instance of Scorer, "
              "nevertheless it is {}({})".format(
                  self.metric,
                  type(self.metric),
              )
          )
      if self.mode not in ("fast", "slow"):
          raise ValueError("Unknown mode %s" % self.mode)
  
      if self.bagging:
          self._bagging(base_models_predictions, true_targets)
      else:
          self._fit(
              predictions=base_models_predictions,
              X_data=X_data,
              labels=true_targets,
          )
      self._calculate_weights()
      self.identifiers_ = model_identifiers
      return self
  
imports: from __future__ import annotations
  from typing import Dict, List, Sequence, Tuple, Union
  import random
  import warnings
  from collections import Counter
  import numpy
  from sklearn.utils import check_random_state
  from autosklearn.automl_common.common.utils.backend import Backend
  from autosklearn.constants import TASK_TYPES
  from autosklearn.data.validation import SUPPORTED_FEAT_TYPES
  from autosklearn.ensemble_building.run import Run
  from autosklearn.ensembles.abstract_ensemble import AbstractEnsemble
  from autosklearn.metrics import Scorer, calculate_losses
  from autosklearn.pipeline.base import BasePipeline
label_desc: Use image node_id 2 for calling the EnsembleSelection obj's underlying member method code with example usage: obj.fit(base_models_predictions, true_targets, model_identifiers, runs, X_data) and returns: self